Tailin Wu

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 41

Top Topics

Robust Classification

Temporal Data Mining

Information Bottleneck

Image Stitching

Top Venues

Publications

Tailin Wu, Willie Neiswanger, Hongtao Zheng, Stefano Ermon, Jure Leskovec
Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution. AAAI (2024)
Haixin Wang, Jiaxin Li, Anubhav Dwivedi, Kentaro Hara, Tailin Wu
BENO: Boundary-embedded Neural Operators for Elliptic PDEs. ICLR (2024)
Haixin Wang, Jiaxin Li, Anubhav Dwivedi, Kentaro Hara, Tailin Wu
BENO: Boundary-embedded Neural Operators for Elliptic PDEs. CoRR (2024)
Tailin Wu, Takashi Maruyama, Long Wei, Tao Zhang, Yilun Du, Gianluca Iaccarino, Jure Leskovec
Compositional Generative Inverse Design. ICLR (2024)
Long Wei, Peiyan Hu, Ruiqi Feng, Haodong Feng, Yixuan Du, Tao Zhang, Rui Wang, Yue Wang, Zhi-Ming Ma, Tailin Wu
A Generative Approach to Control Complex Physical Systems. CoRR (2024)
Tailin Wu, Willie Neiswanger, Hongtao Zheng, Stefano Ermon, Jure Leskovec
Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution. CoRR (2024)
Bryan E. Kaiser, Tailin Wu, Maike Sonnewald, Colin Thackray, Skylar Callis
A Moonshot for AI Oracles in the Sciences. CoRR (2024)
Qianru Zhang, Haixin Wang, Cheng Long, Liangcai Su, Xingwei He, Jianlong Chang, Tailin Wu, Hongzhi Yin, Siu-Ming Yiu, Qi Tian, Christian S. Jensen
A Survey of Generative Techniques for Spatial-Temporal Data Mining. CoRR (2024)
Tailin Wu, Takashi Maruyama, Long Wei, Tao Zhang, Yilun Du, Gianluca Iaccarino, Jure Leskovec
Compositional Generative Inverse Design. CoRR (2024)
Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. CoRR (2023)
Tailin Wu, Takashi Maruyama, Qingqing Zhao, Gordon Wetzstein, Jure Leskovec
Learning Controllable Adaptive Simulation for Multi-resolution Physics. CoRR (2023)
Tailin Wu, Takashi Maruyama, Qingqing Zhao, Gordon Wetzstein, Jure Leskovec
Learning Controllable Adaptive Simulation for Multi-resolution Physics. ICLR (2023)
Yefeng Liang, Shibo Li, Xingyu Li, Yucheng He, Ying Hu, Tailin Wu, Huiren Tao
Robust X-ray Image Stitching Algorithm Based on Refining Matching Results of Feature Descriptors. ISBI (2023)
Zhongyi Han, Guanglin Zhou, Rundong He, Jindong Wang, Tailin Wu, Yilong Yin, Salman H. Khan, Lina Yao, Tongliang Liu, Kun Zhang
How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation. CoRR (2023)
Tailin Wu, Takashi Maruyama, Jure Leskovec
Learning to Accelerate Partial Differential Equations via Latent Global Evolution. NeurIPS (2022)
Tailin Wu, Qinchen Wang, Yinan Zhang, Rex Ying, Kaidi Cao, Rok Sosic, Ridwan Jalali, Hassan Hamam, Marko Maucec, Jure Leskovec
Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator. CoRR (2022)
Michael Skuhersky, Tailin Wu, Eviatar Yemini, Amin Nejatbakhsh, Edward S. Boyden, Max Tegmark
Toward a more accurate 3D atlas of C. elegans neurons. BMC Bioinform. 23 (1) (2022)
Jinhang Li, Shibo Li, Zili Yang, Tailin Wu, Ying Hu
An Automatic Scoliosis Diagnosis Platform Based on Deep Learning Approach. APIT (2022)
Tailin Wu, Qinchen Wang, Yinan Zhang, Rex Ying, Kaidi Cao, Rok Sosic, Ridwan Jalali, Hassan Hamam, Marko Maucec, Jure Leskovec
Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator. KDD (2022)
Daniel Zeng, Tailin Wu, Jure Leskovec
ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy. CoRR (2022)
Tailin Wu, Megan Tjandrasuwita, Zhengxuan Wu, Xuelin Yang, Kevin Liu, Rok Sosic, Jure Leskovec
ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time. CoRR (2022)
Tailin Wu, Takashi Maruyama, Jure Leskovec
Learning to Accelerate Partial Differential Equations via Latent Global Evolution. CoRR (2022)
Tailin Wu, Megan Tjandrasuwita, Zhengxuan Wu, Xuelin Yang, Kevin Liu, Rok Sosic, Jure Leskovec
ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time. NeurIPS (2022)
Yanying Lin, Kejiang Ye, Ming Chen, Naitian Deng, Tailin Wu, Cheng-Zhong Xu
LBNN: Perceiving the State Changes of a Core Telecommunications Network via Linear Bayesian Neural Network. ICPADS (2020)
Silviu-Marian Udrescu, Andrew K. Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, Max Tegmark
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity. NeurIPS (2020)
Tailin Wu, Ian S. Fischer
Phase Transitions for the Information Bottleneck in Representation Learning. ICLR (2020)
Tailin Wu
Intelligence, physics and information - the tradeoff between accuracy and simplicity in machine learning. CoRR (2020)
Tailin Wu, Ian S. Fischer
Phase Transitions for the Information Bottleneck in Representation Learning. CoRR (2020)
Tailin Wu, Hongyu Ren, Pan Li, Jure Leskovec
Graph Information Bottleneck. NeurIPS (2020)
Tailin Wu, Hongyu Ren, Pan Li, Jure Leskovec
Graph Information Bottleneck. CoRR (2020)
Tailin Wu, Thomas M. Breuel, Michael Skuhersky, Jan Kautz
Discovering Nonlinear Relations with Minimum Predictive Information Regularization. CoRR (2020)
Max Tegmark, Tailin Wu
Pareto-Optimal Data Compression for Binary Classification Tasks. Entropy 22 (1) (2020)
Silviu-Marian Udrescu, Andrew K. Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, Max Tegmark
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity. CoRR (2020)
Tailin Wu, Ian S. Fischer, Isaac L. Chuang, Max Tegmark
Learnability for the Information Bottleneck. Entropy 21 (10) (2019)
Tailin Wu, Ian S. Fischer, Isaac L. Chuang, Max Tegmark
Learnability for the Information Bottleneck. UAI (2019)
Max Tegmark, Tailin Wu
Pareto-optimal data compression for binary classification tasks. CoRR (2019)
Tailin Wu, Ian S. Fischer, Isaac L. Chuang, Max Tegmark
Learnability for the Information Bottleneck. CoRR (2019)
Tailin Wu, Max Tegmark
Toward an AI Physicist for Unsupervised Learning. CoRR (2018)
Tailin Wu, John Peurifoy, Isaac L. Chuang, Max Tegmark
Meta-learning autoencoders for few-shot prediction. CoRR (2018)
Curtis G. Northcutt, Tailin Wu, Isaac L. Chuang
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels. CoRR (2017)
Curtis G. Northcutt, Tailin Wu, Isaac L. Chuang
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels. UAI (2017)